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VENKATESAN AND OTHERS
PARASITE GENES AND RESISTANCE TO AL AND ASAQ
Polymorphisms in Plasmodium falciparum Chloroquine Resistance Transporter and
Multidrug Resistance 1 Genes: Parasite Risk Factors that Affect Treatment Outcomes for
P. falciparum Malaria after Artemether-Lumefantrine and Artesunate-Amodiaquine
Meera Venkatesan, Nahla B. Gadalla, Kasia Stepniewska, Prabin Dahal, Christian Nsanzabana, Clarissa
Moriera, Ric N. Price, Andreas Mårtensson, Philip J. Rosenthal, Grant Dorsey, Colin J. Sutherland,
Philippe Guérin, Timothy M. E. Davis, Didier Ménard, Ishag Adam, George Ademowo, Cesar Arze,
Frederick N. Baliraine, Nicole Berens-Riha, Anders Björkman, Steffen Borrmann, Francesco Checchi,
Meghna Desai Mehul Dhorda, Abdoulaye A. Djimdé, Badria B. El-Sayed, Teferi Eshetu, Frederick
Eyase, Catherine Falade, Jean-François Faucher, Gabrielle Fröberg, Anastasia Grivoyannis, Sally
Hamour, Sandrine Houzé, Jacob Johnson, Erasmus Kamugisha, Simon Kariuki, Jean-René Kiechel, Fred
Kironde, Poul-Erik Kofoed Jacques LeBras, Maja Malmberg, Leah Mwai, Billy Ngasala, Francois
Nosten, Samuel L. Nsobya, Alexis Nzila Mary Oguike, Sabina Dahlström Otienoburu, Bernhards Ogutu,
Jean-Bosco Ouédraogo, Patrice Piola, Lars Rombo, Birgit Schramm, A. Fabrice Somé, Julie Thwing,
Johan Ursing, Rina P. M. Wong, Ahmed Zeynudin, Issaka Zongo, Christopher V. Plowe, and Carol
Hopkins Sibley*
WorldWide Antimalarial Resistance Network Molecular Module, Howard Hughes Medical Institute/Center for Vaccine
Development, and Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland; National
Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland; Department of Epidemiology,
Tropical Medicine Research Institute, Khartoum, Sudan; World Wide Antimalarial Resistance Network, Centre for Tropical
Medicine, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom; Global Health Division,
Menzies School of Health Research and Charles Darwin University, Darwin, Northern Territory, Australia; Malaria Research,
Infectious Diseases Unit, Department of Medicine Solna, Stockholm, Sweden; Global Health, Department of Public Health
Sciences, Karolinska Institutet, Stockholm, Sweden; Department of Medicine, University of California, San Francisco, San
Francisco, California; Department of Immunology and Infection, Faculty of Infectious and Tropical Diseases, London School
of Hygiene and Tropical Medicine, London, United Kingdom; School of Medicine and Pharmacology, The University of
Western Australia, Fremantle Hospital, Nedlands, Western Australia, Australia; Malaria Molecular Epidemiology Unit,
Institut Pasteur du Cambodge, Phnom Penh, Cambodia; Faculty of Medicine, University of Khartoum, Khartoum, Sudan;
Institute for Advanced Medical Research and Training, College of Medicine, and Department of Pharmacology and
Therapeutics, University of Ibadan; Ibadan, Nigeria; Department of Biology, LeTourneau University, Longview, Texas;
Department of Infectious Diseases and Tropical Medicine, Ludwig Maximilians University, Munich, Germany; Kenya Medical
Research Institute/Wellcome Trust Research Programme, Kilifi, Kenya; Department of Microbiology, Magdeburg University
School of Medicine, Magdelburg, Germany; Save the Children, Paris, France; Malaria Branch, Division of Parasitic Diseases
and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, Georgia; Epicentre Uganda
Research Base, Mbarara, Uganda; Malaria Research and Training Centre, Faculty of Pharmacy, University of Science,
Techniques and Technologies of Bamako, Bamako, Mali; Department of Medical Laboratory Sciences and Pathology, Medical
Parasitology Unit, Jimma University, Jimma, Ethiopia; Global Emerging Infections System, U.S. Army Research Unit-Kenya
Walter Reed/Kenya Medical Research Institute Project, Kisumu, Kenya; Department of Infectious Diseases, University
Medical Center, Besançon, France; Mère et Enfant Face aux Infections Tropicales, Institut de Recherche pour le
Développement, Paris, France; Department of Medicine, Division of Emergency Medicine, and Department of Genome
Sciences, University of Washington, Seattle, Washington; University College London Centre for Nephrology, Royal Free
Hospital, London, United Kingdom; Laboratory of Parasitology, Malaria National Reference Centre, Assistance Publique–
Hôpitaux de Paris, Bichât Hospital, Paris France; PRES Sorbonne Paris Cité, Faculté de Pharmacie, Université Paris
Descartes, Paris, France; United States Army Medical Research Unit-Kenya, Nairobi, Kenya; Catholic University of Health
and Allied Sciences-Bugando, Mwanza, Tanzania; Malaria Branch, Kenya Medical Research Institute/Centers for Disease
Control and Prevention, Kisumu, Kenya; Drugs for Neglected Diseases initiative, Geneva, Switzerland; Makerere University
College of Health Sciences, Kampala, Uganda; St. Augustine International University, Kampala, Uganda; Projecto de Saude
In order to provide our readers with timely access to new content, papers accepted by the American Journal of Tropical Medicine and Hygiene are posted online ahead of print publication. Papers that have been accepted for publication are peer-reviewed and copy edited but do not incorporate all corrections or constitute the final versions that will appear in the Journal. Final, corrected papers will be published online concurrent with the release of the print issue. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
http://ajtmh.org/cgi/doi/10.4269/ajtmh.14-0031The latest version is at Accepted for Publication, Published online July 21, 2014; doi:10.4269/ajtmh.14-0031.
Copyright 2014 by the American Society of Tropical Medicine and Hygiene
de Bandim, INDEPTH Network, Bissau, Guinea-Bissau; Division of Translational Therapeutics, Department of Paediatrics,
Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada; Department of Parasitology,
Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania; Centre for Tropical Medicine, Nuffield
Department of Medicine, University of Oxford, Oxford, United Kingdom; Shoklo Malaria Research Unit, Mahidol-Oxford
Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand; Department of
Pathology, School of Biomedical Science, Makerere University College of Health Sciences, Kampala, Uganda; Department of
Biology, King Fahd University of Petroleum and Minerals, Dharan, Saudi Arabia; Worldwide Antimalarial Resistance
Network, Institut de Médecine et d'Epidémiologie Appliquée, Bichât-C. Bernard Hospital, Paris, France; Institut de Recherche
en Sciences de la Santé, Centre Muraz, Bobo-Dioulasso, Burkina Faso; Institut Pasteur du Madagascar, Antananarivo,
Madagascar; Epicentre, Paris, France
* Address correspondence to Carol Hopkins Sibley, Department of Genome Sciences, University of Washington, Box 355586, Seattle, WA
98195. E-mail: [email protected]
Abstract.
Adequate clinical and parasitologic cure by artemisinin combination therapies relies on the artemisinin component and the
partner drug. Polymorphisms in the Plasmodium falciparum chloroquine resistance transporter (pfcrt) and P. falciparum
multidrug resistance 1 (pfmdr1) genes are associated with decreased sensitivity to amodiaquine and lumefantrine, but effects of
these polymorphisms on therapeutic responses to artesunate-amodiaquine (ASAQ) and artemether-lumefantrine (AL) have not
been clearly defined. Individual patient data from 31 clinical trials were harmonized and pooled by using standardized methods
from the WorldWide Antimalarial Resistance Network. Data for more than 7,000 patients were analyzed to assess relationships
between parasite polymorphisms in pfcrt and pfmdr1 and clinically relevant outcomes after treatment with AL or ASAQ.
Presence of the pfmdr1 gene N86 (adjusted hazards ratio = 4.74, 95% confidence interval = 2.29 – 9.78, P < 0.001) and
increased pfmdr1 copy number (adjusted hazards ratio = 6.52, 95% confidence interval = 2.36–17.97, P < 0.001) were
significant independent risk factors for recrudescence in patients treated with AL. AL and ASAQ exerted opposing selective
effects on single-nucleotide polymorphisms in pfcrt and pfmdr1. Monitoring selection and responding to emerging signs of
drug resistance are critical tools for preserving efficacy of artemisinin combination therapies; determination of the prevalence
of at least pfcrt K76T and pfmdr1 N86Y should now be routine.
INTRODUCTION
Recent successes in malaria control have depended on the use of highly efficacious artemisinin
combination therapies (ACTs) for first-line treatment of uncomplicated Plasmodium falciparum malaria.
Adequate clinical and parasitologic cure by ACTs relies on the rapid reduction in parasite biomass by the
potent, short-acting artemisinin component1–3
and the subsequent elimination of residual parasites by the
longer-acting partner drug. The two most commonly used ACTs worldwide are artemether-lumefantrine
(AL) and artesunate-amodiaquine (ASAQ).4 Polymerase chain reaction (PCR)–adjusted efficacy for both
combinations remains high in most regions.5–7
However, there have been some reports of decreasing AL
cure rates in Africa8–11
and Asia,12
and reports of high levels of treatment failures of ASAQ.13–18
Resistance to ACT partner drugs has historically manifested before that of artemisinins, whose short half-
lives result in the exposure of residual parasites to sub-therapeutic levels of the partner drug alone.
Response to the partner drug is therefore a key component of overall ACT efficacy.
Mutations in the gene encoding the P. falciparum chloroquine resistance transporter (pfcrt) are
associated with chloroquine resistance19
; a change from lysine to threonine at codon 76 in pfcrt predicts
responses of parasites to chloroquine.20,21
In the presence of pfcrt 76T, chloroquine resistance is
modulated by point mutations in the gene that encodes the P. falciparum multidrug resistance transporter
1 (pfmdr1), primarily at codon 8622,23
and also by mutations at positions 1034, 1042, and 1246.24
Decreased susceptibility to lumefantrine has been linked to polymorphisms in these two genes.25–35
Increased pfmdr1 copy number, which confers resistance to mefloquine,36
has also been associated with
reduced susceptibility to lumefantrine.37–40
Studies of amodiaquine have demonstrated reduced in vivo response41–43
and increased 50% inhibitory
concentration values in vitro, in association with the presence of pfmdr1 86Y and pfcrt 76T alleles.44,45
Selection of these alleles in recurrent parasites after treatment with amodiaquine alone or in combination
with artesunate has been observed in a number of studies.28,46–51
It has also been suggested that parasites
that carry chloroquine-resistant pfmdr1 alleles may be more susceptible to artesunate in classical in vitro
assays,24,52
an effect that could counteract the increased risk of amodiaquine failure when these drugs are
combined in ASAQ.
Currently, AL and ASAQ retain high clinical efficacy with few recrudescent infections, and individual
studies generally lack sufficient statistical power to assess the association between parasite genotypes and
outcomes of clinical treatment. Such an assessment is a critical step in validating molecular changes in
parasite populations as useful markers of early signs of changing parasite susceptibility to lumefantrine or
amodiaquine. To overcome these challenges, individual patient data on in vivo antimalarial efficacy and
molecular markers of P. falciparum from 31 clinical trials were standardized, pooled, and > 7,000 patient
responses were analyzed to determine whether patients infected with parasites that carry these
polymorphisms are at increased risk of treatment failure. This large data set also provided the opportunity
to examine the effects of AL and ASAQ treatment on selection in parasites of particular alleles of pfcrt
and pfmdr1.
METHODS
Selection and inclusion of data.
Prospective clinical efficacy studies of P. falciparum treatment with AL (six-dose regimen) or ASAQ
(three-day fixed dose or loose/co-blistered regimen) with a minimum of 28 days of follow-up and
genotyping of pfcrt and/or pfmdr1 were sought for the analysis. Studies were identified by a systematic
PubMed literature review using the search terms (artesunate AND amodiaquine) OR (artemether AND
lumefantrine) OR (ACT) AND (pfmdr1 OR pfcrt). Abstracts and text were screened to determine whether
inclusion criteria were met. Unpublished datasets were also solicited and included in the analysis.
Individual anonymized patient data including baseline characteristics, drug intake, parasite density and
temperature were collected. All but one study included parasite genotyping to identify recrudescent
infections of P. falciparum, and all studies assessed the presence of pfcrt and/or pfmdr1 polymorphisms
(single nucleotide polymorphisms (SNPs) and copy number variation) in parasites isolated from patients
on day 0. Multiplicity of infection and molecular resistance marker data from other days including the day
of microscopic recurrent parasitemia were included but were not a prerequisite for study inclusion.
Metadata on study location, study design, drugs, and dosing regimens were also gathered. A schematic of
the patient numbers and overall flow of the study is shown in Figure 1.
Data curation and generation of variables.
All data sets were uploaded to the WorldWide Antimalarial Resistance Network repository and
standardized by using the WorldWide Antimalarial Resistance Network Data Management and Statistical
Analysis Plans (DMSAP).53,54
Outcome status and censoring were defined according to the Clinical
DMSAP.53
Parasites that recurred within the follow-up period were classified using World Health
Organization guidelines55
: microscopically detected infections during follow-up were classified as
recurrent; recurrent infections sharing with blood samples taken at day 0 PCR bands in polymorphic
merozoite antigens or microsatellite fragment sizes were classified as recrudescent, and recurrent
infections not sharing PCR bands or microsatellite fragment sizes with blood samples taken at day 0 were
classified as re-infections (new infections). Molecular markers were coded as either single or mixed allele
genotypes in the case of SNPs and as mean copy number per sample for copy number polymorphisms.
Multi-SNP haplotypes were reconstructed as described in the Molecular DMSAP.56,57
Statistical analysis.
All statistical analyses were conducted by using Stata 11 (StataCorp LP, College Station, TX). The
primary endpoint was clinical efficacy, defined as the PCR-adjusted risk of P. falciparum recrudescent
infections. The cumulative risk of recrudescence at day 28 and day 42 was computed by using survival
analysis (Kaplan-Meier estimates [K-M]). Comparisons of K-M survival curves were performed by using
log rank tests stratified by study sites.
Multivariable analysis of risk factors associated with PCR-adjusted recrudescence was conducted by
using Cox proportional hazards regression models with shared frailty parameters to adjust for site-specific
effects. The risk factors that affect the clinical efficacy of AL and ASAQ have been intensively studied in
pooled analyses of both ACTs. Sixty-two studies with 14,651 patients treated with AL and 39 studies with
8,337 patients treated with ASAQ were analyzed; these full analyses have been submitted for publication.
The univariable and multivariable risk factors identified in those studies are shown in Supplemental
Tables 1 and 2. Clinical covariates in the current study were included based on the previous analyses as
follows: (lumefantrine or amodiaquine dose, enrolment parasitemia, age category, and ASAQ fixed or co-
blistered versus loose formulation (Table 1). Each molecular marker was then added to the model. The
proportional hazard assumption was tested based on Schoenfeld residuals of Schoenfeld.58
In the case of
non-proportionality, interactions with a categorized time variable based on clinical follow-up intervals (<
day 14, days 14–21, 21–28, and > day 28) were used to account for changing effects over time, and
neighboring windows with similar effects of genetic covariates as determined by Wald test were merged.
Finally, other covariates (transmission intensity, region of sample origin, dose supervision, and fat intake)
were included in the model if they improved model fit based on the likelihood ratio test. Multiplicity of
infection was only available for 197 and 141 AL and ASAQ patients, respectively, and was excluded from
further analysis. The final model was then used to estimate the adjusted hazard ratio for recrudescence in
patients who carried parasites with resistant versus sensitive genotypes on day 0. The assumption of
proportional hazards was tested separately for the individual covariates in the final multivariable model,
and any violations were reported.
In patients who had recurrent parasitemia on or before day 42, changes in pfcrt and pfmdr1 alleles
between pre-treatment and post-treatment matched pairs of samples was compared by using McNemar’s
test. Changes in genotype, rather than presence of a particular allele, were compared between matched
pairs to ensure that differences reflected selection rather than underlying differences in allele frequencies
among populations. The effect of markers present at the time of recurrence on median time to PCR-
adjusted re-infection (new infection) was investigated by using the Wilcoxon Mann-Whitney U test.
Competing risk analysis59
was used to estimate cumulative incidence of PCR-adjusted re-infections with
specific genotypes, where recrudescent and re-infections with other genotypes were treated as competing
events.
The number of molecular markers used to distinguish recrudescence from re-infection varied from one
to three or more loci. The effect of the number of loci genotyped on outcome classification was
investigated in a regression model of predictors of recrudescence within all recurrences. No effect of this
variable was observed on the number of recrudescent infections identified among recurrences in
univariable or multivariable analysis, it was not further investigated.
RESULTS
Individual patient and linked parasite genotype data from 31 studies were available (Supplemental
Table 3). Data from 7,249 patients who were treated with AL (5,003) or ASAQ (2,246) were included in
the analysis. Twenty two studies were published, representing 91% of all published clinical data on AL
and ASAQ in which pfcrt or pfmdr1 genotypes were determined. Baseline characteristics for patients
treated with AL or ASAQ are shown in Supplemental Table 4.
Clinical efficacy of AL and ASAQ.
The estimates of efficacy (defined as risk of PCR-adjusted recrudescence) of AL and ASAQ are
shown in Table 2. Of the 5,003 AL patients, 4,763 were followed-up for at least one day and were
included in the analysis. Similarly, of the 2,246 ASAQ patients, 2,099 were included. In total, 1,107
patients had recurrent parasitemia after treatment with AL, of 188 (18%) were classified by PCR as
having recrudescent infections. The corresponding figures for ASAQ were 484 patients had recurrent
parasitemia and 58 (12%) were confirmed as having recrudescent infections. The overall clinical efficacy
at day 42 was 94.8% (95% confidence interval [CI] = 94–95.5%) in patients treated with AL and 95.1%
(95% CI = 92.3–96.7%) in patients treated with ASAQ (Table 2). The proportion of adequate clinical and
parasitologic response of ASAQ was significantly higher for the fixed dose and co-blistered tablets
(97.0%, 95% CI = 94.4–98.4%) compared with the loose formulation (93.0%, [95% CI = 89.2–95.6) (P =
0.003).
Baseline prevalence of genetic markers associated with resistance.
The baseline prevalence of SNPs in pfcrt and pfmdr1 was determined, but not all SNPs were available
for all isolates. The most frequently analyzed SNPs were position 76 in pfcrt determined for 3,640
patients and position 86 in pfmdr1 for 3,580 patients, with the complete haplotype of positions 72–76 in
pfcrt, pfmdr1 copy number, and SNPs at positions pfmdr1 184, 1034, 1042, and 1246 available in a subset
of patients (Table 3).
The prevalence of pfcrt and pfmdr1 alleles varied by region (Table 3). The pfcrt 76T allele (all in the
SVMNT haplotype) was almost fixed at 96.4% (81/84) in isolates from Asia (Thailand) and Oceania
(Papua New Guinea). In Africa, the only resistant haplotype observed was the CVIET allele. The 76T
allele predominated: 67.6% (1,155/1,708) in East Africa and 73.3% (1,354/1,848) in West Africa (Table
3). Amplification of pfmdr1 was seen in 50% (88/176) of isolates from Asia examined for this genotype,
but only in 2.4% (16/659) of isolates from Africa. Pfmdr1 86Y was found in 29.2% (66/226) of isolates
from Asia/Oceania; in contrast, the 86Y allele was present in 44.1% (896/2,033) of isolates from East
Africa and 34.3% (453/1,321) of isolates from West Africa.
The SNPs at positions 184 and 1246 showed similar patterns, with pfmdr1 Y184 and D1246
predominating in all three regions (Table 3). Almost all isolates examined carried the pfmdr1 S1034
(760/844) and N1042 (1,053/1,064).
Parasite genotypes as risk factors for recrudescent infection.
After controlling for age, baseline parasite density, and total lumefantrine dose (Table 1), the presence
of parasites in the initial infection that carried pfmdr1 N86 (alone or a mixed infection with pfmdr1 86Y)
was a significant risk factor for recrudescent infection occurring between days 14 and 28 after AL
treatment (adjusted hazards ratio [AHR] = 4.74, 95% CI = 2.29–9.78, P < 0.001) (Table 4 and Figure 2A).
Region of sample origin was not included as a covariate in the model because it violated the assumption
of proportional hazards. The risk associated with presence of pfmdr1 N86 remained significant when
excluding infections with multiple copies of pfmdr1 (AHR = 3.93, 95% CI = 1.90–8.94, P < 0.001). The
region of sample origin interacted significantly with pfmdr1 N86, showing that the marker had a larger
effect in Asia (AHR = 14.06, 95% CI = 4.52–43.74, P < 0.001) than in Africa (AHR = 3.72, 95% CI =
1.77–7.79, P = 0.001). However, this interaction violated the proportional hazards assumption since there
were so few samples in Africa that had multiple copies of pfmdr1, and this variable was excluded from
the final model.
The presence of more than one copy of pfmdr1 was a significant risk factor for recrudescence
occurring between days 14 and 21 after AL treatment (AHR = 5.81, 95% CI = 2.38–14.21, P < 0.001)
(Figure 2B). When the effect of region of origin was added to the model, patients with parasites carrying
multiple copy numbers of pfmdr1 were associated with an increased risk of recrudescence before day 14
(AHR = 83.56, 95% CI = 7.43–939.70, P < 0.001) as well as between days 14 and 21 (AHR = 18.54 (95%
CI = 7.61–45.19, P < 0.001) (Table 4). The interaction of region of origin with pfmdr1 copy number could
not be investigated because of insufficient multicopy samples from Africa in the model.
When pfmdr1 N86 and pfmdr1 copy number were included in the same model, region of sample
origin was no longer a significantly predictive covariate in the multivariable analysis or as an interaction
term with either genotype. Both markers remained as significant predictors of recrudescent infection,
between days 14 and 28 for pfmdr1 N86 (AHR = 5.98, 95% CI = 1.68–21.36, P = 0.006) and days 14 and
21 for multiple copies of pfmdr1 (AHR = 6.52, 95% CI = 2.36–17.97, P < 001); Table 4)
No association was observed between the pfmdr1 184, pfmdr1 1246, and pfcrt polymorphisms and
recrudescent infections after AL treatment. The risk for parasites with the pfmdr1 N86 + D1246 haplotype
is not reported here because it represents a subset of the pfmdr1 N86 sample set (of the samples genotyped
for both SNPs, all but 17 samples with pfmdr1 N86 also had D1246). For patients treated with ASAQ,
none of the analyzed pfcrt or pfmdr1 parasite genotypes were significant risk factors for recrudescent
infections in the multivariable analysis.
Post-treatment selection of genetic markers associated with resistance.
To examine changes in the genotypes of parasites after drug treatment, we compared the prevalence of
pfmdr1 and pfcrt alleles in paired isolates from the initial and the recurrent parasites in the subset of
patients in whom parasites recurred during the 42 day follow-up period. Post-treatment changes among
specific genotypes are shown in Table 5 for all recurrent infections. Significant selection of pfcrt K76,
pfmdr1 N86 occurred in recrudescent and re-infecting parasites after AL treatment. Selection of pfmdr1
184F and D1246 alleles was also observed in the recurrent parasites and pfmdr1 D1246 in those that
reinfected patients after treatment. Selection of single or multiple copies of pfmdr1 was not observed in
any of the groups (Table 5). Pfmdr1 86Y and 1246Y were significantly selected in recurrent and re-
infections after treatment with ASAQ (Table 5).
Median time to re-infection.
The genotype of parasites at the time of re-infection provides another metric of their susceptibility to a
drug. This analysis indicated that in patients treated with AL, re-infecting parasites carrying pfmdr1 N86,
pfmdr1 D1246, or pfcrt K76 alleles appeared earlier than those carrying pfmdr1 86Y, pfmdr1 1246Y, or
pfcrt 76T (Figure 3A). Correspondingly, in patients treated with AL, parasites carrying pfmdr1 N86 had a
median time to re-infection of 28 days (interquartile range = 21–35 days) compared with 35 days
(interquartile range = 28–42 days) for those with pfmdr1 86Y (P < 0.001). Similar differences in the time
to re-infection were observed for patients infected with parasites that carried the pfmdr1 184F (P = 0.008)
or pfcrt K76 alleles (P = 0.001) compared with pfmdr1 Y184 or pfcrt 76T.
In contrast, in patients treated with ASAQ, parasites carrying pfmdr1 86Y, pfmdr1 1246Y, or pfcrt
76T appeared earlier after treatment than those carrying pfmdr1 N86, pfmdr1 D1246 or pfcrt K76 (Figure
3B). Parasites with pfcrt 76T had a median reinfection day of 28 (interquartile range = 21–35) compared
with day 37.5 (interquartile range = 28–42) for those carrying K76 (P = 0.053) and those with pfmdr1
1246Y re-infected on a median day of 21 (interquartile range = 21–28) compared with day 28
(interquartile range = 21–35) for those with D1246 (P = 0.001).
DISCUSSION
This pooled analysis of data from 31 clinical studies shows clearly that the genotypes of infecting
parasites influence the outcome of AL treatment. Patients infected with parasites that carried the pfmdr1
N86 allele or increased pfmdr1 copy number were at significantly greater risk of treatment failure than
those whose parasites carried the 86Y allele or a single copy of pfmdr1. Analysis of the clinical outcomes
after treatment with ASAQ did not link a particular genotype with treatment failure in this smaller data
set. However, it did show clear evidence of selection of particular parasite genotypes. Our findings are
consistent with those of previous molecular studies in which changes in the prevalence of particular
alleles of pfcrt or pfmdr1 have been documented in response to introduction or increased use of
lumefantrine 25–35
or amodiaquine.15,28,40–51
Our observation that parasites with the pfmdr1 N86, D1246, and pfcrt K76 alleles re-infected patients
earlier after AL treatment, and parasites carrying the pfmdr1 86Y, 1246Y, and pfcrt 76T alleles re-
infected patients earlier after ASAQ is also congruent with the molecular studies. These differences
suggest that parasites with these genotypes can withstand higher drug concentrations compared with
parasites that carry the alternative alleles. Recently, Malmberg and others33
demonstrated this effect
quantitatively. After AL treatment, parasites with the pfmdr1 N86/184F/D1246 haplotype were able to re-
infect patients whose lumefantrine blood concentrations were 15-fold higher than was the case for
parasites carrying the 86Y/Y184/1246Y haplotype,33
providing a potential pharmacologic explanation for
the molecular findings. Together, these observations suggest that monitoring shifts to earlier time of re-
infection could provide a relatively simple warning of decreasing susceptibility to these drugs, especially
if combined with timed measurement of drug concentrations in patients’ blood.
In Southeast Asia, parasites with increased pfmdr1 copy number are common in areas where
mefloquine has been intensively deployed,36
and almost half of the samples in our data set from that
region had at least two copies of the gene. Increased pfmdr1 copy number was rarely observed in our
large sample of isolates from Africa, populations that have had little exposure to mefloquine.
Lumefantrine has a shorter half-life in patients than mefloquine,60
and may not exert an equivalently
strong selection for copy number increase. However, in areas where mefloquine is being introduced, close
attention to pfmdr1 copy number is clearly warranted. A recent report of parasites in Ghana with
increased pfmdr1 copy number underscores the importance of including this parameter in molecular
surveillance.61
This study supported the conclusion that parasites with increased copy number of pfmdr1 are also less
sensitive to lumefantrine.37–40
In Southeast Asia, the amplified alleles almost always carried the N86 allele
of pfmdr1.34,36,62
However, this was not the case in the few parasites from Africa in our data set that did
have an increased copy number31
so either of the N86Y alleles of pfmdr1 can apparently be amplified. It
is also important to note that increased copy number and the presence of the pfmdr1 N86 allele were
independent risk factors for treatment failure in our analysis.
The evidence of strong selection of particular alleles by both drugs in recurrent parasites, coupled with
our observation that particular parasite genotypes increase risk of treatment failure, demonstrates that
tracking these molecular markers can signal early decreases in susceptibility to lumefantrine or
amodiaquine. Both alleles of pfmdr1 N86Y, Y184F, and D1246Y are common in P. falciparum
populations I Africa, and pfcrt K76 has increased in prevalence in recent years. Thus, changes in the
prevalence of these alleles can be a sensitive indicator of selection of parasite populations by AL and
ASAQ. In turn, decreasing efficacy of these partner drugs exposes the artemether or artesunate component
of the ACT to selective pressure and could facilitate emergence of new foci of resistance to artemisinin, as
observed in the Mekong region. The recent identification of a marker correlated with slow response to
artemisinin,63
will also enable molecular assessment of this trend.
Application of these molecular tools is increasingly feasible in the context of clinical trials and in
community surveys of populations where AL or ASAQ are heavily used. These approaches can offer cost-
effective methods that detect evidence of declines in parasite susceptibility far earlier than before,
enabling detailed studies of clinical responses to the drugs in areas of concern. This two-stage approach
can provide an opportunity for policy makers to manage emerging threats of resistance before clinical
failure of a drug is manifest and preserve the useful therapeutic life of these valuable antimalarial drugs
for as long as possible.
Finally, these results suggest that AL and ASAQ interact with the proteins encoded by pfcrt and
pfmdr1, but the two drugs select alternative alleles. Two recent publications have also demonstrated that
piperaquine exerts selection pressure on these genes in the same direction as amodiaquine, suggesting that
the newer ACT, dihydroartemisinin-piperaquine could also function as a counterweight to
lumefantrine.64,65
This opposing selection of parasite genotypes by the partner drugs could influence the
choice of an ACT in regions with different patterns of pfcrt and pfmdr1 polymorphisms. For example, if a
particular allele is rapidly increasing under intensive use of AL, introduction of AQ or piperaquine might
be introduced to counteract that trend. Concurrent use of two ACTs that exert opposing selective
pressures on recurrent parasites could provide a counterbalance and prevent strong directional selection in
pfcrt and pfmdr1, maintaining the overall efficacy of AL and ASAQ for a long period. Despite logistical
challenges, the simultaneous use of multiple first line therapies is supported by mathematical models,66–68
and concurrent availability of AL and ASAQ, as implemented in some countries in West Africa4 may
provide a practical means to test this strategy directly.
Received January 14, 2014.
Accepted for publication April 22, 2014.
Note: Supplemental tables appear at www.ajtmh.org.
Financial support: The WorldWide Antimalarial Resistance Network is supported by the Bill and Melinda Gates Foundation.
Disclaimer: The opinions and assertions contained herein are the personal opinions of authors and are not to be construed as
reflecting the views of the U.S. Army Medical Research Unit-Kenya or the U.S. Department of Defense.
Authors’ addresses: Meera Venkatesan and Christopher V. Plowe, WorldWide Antimalarial Resistance Network Molecular
Module and Howard Hughes Medical Institute/Center for Vaccine Development, University of Maryland School of Medicine,
Baltimore, MD. Nahla B. Gadalla National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda,
MD, and Department of Epidemiology, Tropical Medicine Research Institute, Khartoum, Sudan. Kasia Stepniewska, Prabin
Dahal, Christian Nsanzabana, and Clarissa Moriera, World Wide Antimalarial Resistance Network, Centre for Tropical
Medicine, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom. Ric N. Price, World
Wide Antimalarial Resistance Network, Centre for Tropical Medicine, Nuffield Department of Clinical Medicine, University
of Oxford, Oxford, United Kingdom, and Global Health Division, Menzies School of Health Research and Charles Darwin
University, Darwin, Northern Territory, Australia. Andreas Mårtensson, Malaria Research, Infectious Diseases Unit,
Department of Medicine Solna, Stockholm, Sweden, and Global Health, Department of Public Health Sciences, Karolinska
Institutet, Stockholm, Sweden. Philip J. Rosenthal and Grant Dorsey, Department of Medicine, University of California, San
Francisco, San Francisco, CA. Colin J. Sutherland and Mary Oguike, Department of Immunology and Infection, Faculty of
Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom. Philippe
Guérin, World Wide Antimalarial Resistance Network, Centre for Tropical Medicine, Nuffield Department of Clinical
Medicine, University of Oxford, Oxford, United Kingdom. Timothy M. E. Davis and Rina P. M. Wong, School of Medicine
and Pharmacology, The University of Western Australia, Fremantle Hospital, Nedlands, Western Australia, Australia. Didier
Ménard, Malaria Molecular Epidemiology Unit, Institut Pasteur du Cambodge, Phnom Penh, Cambodia. Ishag Adam, Faculty
of Medicine, University of Khartoum, Khartoum, Sudan. George Ademowo, Institute for Advanced Medical Research and
Training, College of Medicine, University of Ibadan, Ibadan, Nigeria. Cesar Arze, WorldWide Antimalarial Resistance
Network Molecular Module and Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD.
Frederick N. Baliraine, Department of Biology, LeTourneau University, Longview, TX. Nicole Berens-Riha, Department of
Infectious Diseases and Tropical Medicine, Ludwig Maximilians University, Munich, Germany. Anders Björkman, Gabrielle
Fröberg, Maja Malmberg, Lars Rombo, and Johan Ursing, Malaria Research, Infectious Diseases Unit, Department of
Medicine Solna, Stockholm, Sweden. Steffen Borrmann, Kenya Medical Research Institute/Wellcome Trust Research
Programme, Kilifi, Kenya, and Department of Microbiology, Magdeburg University School of Medicine, Magdelburg,
Germany. Francesco Checchi, Save the Children, Paris, France. Meghna Desai and Julie Thwing, Malaria Branch, Division of
Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, GA. Mehul
Dhorda, WorldWide Antimalarial Resistance Network Molecular Module, University of Maryland School of Medicine,
Baltimore, MD, and Epicentre Uganda Research Base, Mbarara, Uganda. Abdoulaye A. Djimdé, Malaria Research and
Training Centre, Faculty of Pharmacy, University of Science, Techniques and Technologies of Bamako, Bamako, Mali. Badria
B. El-Sayed, Department of Epidemiology, Tropical Medicine Research Institute, Khartoum, Sudan. Teferi Eshetu and Ahmed
Zeynudin, Department of Medical Laboratory Sciences and Pathology, Medical Parasitology Unit, Jimma University, Jimma,
Ethiopia. Frederick Eyase and Bernhards Ogutu, Global Emerging Infections System, U.S. Army Research Unit-Kenya Walter
Reed/Kenya Medical Research Institute Project, Kisumu, Kenya. Catherine Falade, Department of Pharmacology and
Therapeutics, University of Ibadan, Ibadan, Nigeria. Jean-François Faucher, Department of Infectious Diseases, University
Medical Center, Besançon, France, and Institut de Recherche pour le Développement, Paris, France. Anastasia Grivoyannis,
Department of Medicine, Division of Emergency Medicine, University of Washington, Seattle, WA. Sally Hamour, University
College London Centre for Nephrology, Royal Free Hospital, London, United Kingdom. Sandrine Houzé and Jacques LeBras,
Laboratory of Parasitology, Malaria National Reference Centre, Assistance Publique–Hôpitaux de Paris, Bichât Hospital, Paris
France, Institut de Recherche pour le Développement, Mère et Enfant Face aux Infections Tropicales, Paris, France, and
Faculté de Pharmacie, PRES Sorbonne Paris Cité, Université Paris Descartes, Paris, France. Jacob Johnson, U.S. Army
Medical Research Unit-Kenya, Nairobi, Kenya. Erasmus Kamugisha, Catholic University of Health and Allied Sciences-
Bugando, Mwanza, Tanzania. Simon Kariuki, Malaria Branch, Kenya Medical Research Institute/Centers for Disease Control
and Prevention, Kisumu, Kenya. Jean-René Kiechel, Drugs for Neglected Diseases Initiative, Geneva, Switzerland. Fred
Kironde, Makerere University College of Health Sciences, Kampala, Uganda, and St. Augustine International University,
Kampala, Uganda. Poul-Erik Kofoed, Projecto de Saude de Bandim, INDEPTH Network, Bissau, Guinea-Bissau. Leah Mwai,
Division of Translational Therapeutics, Department of Paediatrics, Faculty of Medicine, University of British Columbia,
Vancouver, British Columbia, Canada. Billy Ngasala, Department of Parasitology, Muhimbili University of Health and Allied
Sciences, Dar es Salaam, Tanzania. Francois Nosten, Centre for Tropical Medicine, Nuffield Department of Medicine,
University of Oxford, Oxford, United Kingdom, and Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine
Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand. Samuel L. Nsobya, Department of
Pathology, School of Biomedical Science, Makerere University College of Health Sciences, Kampala, Uganda. Alexis Nzila,
Department of Biology, King Fahd University of Petroleum and Minerals, Dharan, Saudi Arabia. Sabina Dahlström
Otienoburu, Worldwide Antimalarial Resistance Network, Institut de Médecine et d'Epidémiologie Appliquée, Bichât-C.
Bernard Hospital, Paris, France. Jean-Bosco Ouédraogo, A. Fabrice Somé, and Issaka Zongo, Institut de Recherche en
Sciences de la Santé, Centre Muraz, Bobo-Dioulasso, Burkina Faso. Patrice Piola, Institut Pasteur du Madagascar,
Antananarivo, Madagascar. Birgit Schramm, Epicentre, Paris, France. Carol Hopkins Sibley, World Wide Antimalarial
Resistance Network, Centre for Tropical Medicine, Nuffield Department of Clinical Medicine, University of Oxford, Oxford,
United Kingdom, and Department of Genome Sciences, University of Washington, Seattle, WA.
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FIGURE 1. Patient flow chart for study of parasite risk factors that affect treatment outcomes for Plasmodium falciparum
malaria after treatment with artemether-lumefantrine (AL) and artesunate-amodiaquine (ASAQ).
FIGURE 2. Polymerase chain reaction–adjusted efficacy as assessed by Kaplan-Meier survival estimates for artemether-
lumefantrine (AL) by Plasmodium falciparum multidrug resistance 1 (pfmdr1) genotype of initial parasites. Dotted line
indicates World Health Organization–recommended 90% efficacy cutoff value for antimalarial drugs. Clinical response of
patients with parasites that carry A, pfmdr1 86Y (blue) versus 86N or N/Y (red); n = 2,543 patients at risk and B, pfmdr1 copy
number > 1 (yellow) versus single copy (green); n = 808 patients. This figure appears in color at www.ajtmh.org.
FIGURE 3. A, Cumulative (left panels) and relative (right panels) risks of polymerase chain reaction (PCR)–adjusted reinfection
for baseline Plasmodium falciparum chloroquine resistance transporter (pfcrt) and P. falciparum multidrug resistance 1
(pfmdr1) genotypes after artemether-lumefantrine treatment, in which recrudescent and re-infections with other genotypes were
treated as competing events. B, Cumulative (left panels) and relative (right panels) risks of PCR-adjusted re-infection for
baseline pfcrt and pfmdr1 genotypes after artesunate-amodiaquine treatment, in which recrudescent and re-infections with other
genotypes were treated as competing events. This figure appears in color at www.ajtmh.org.
TABLE 1
Multivariable risk factors for PCR-adjusted recrudescent infections for persons treated with artemether-lumefantrine and
artesunate-amodiaquine at day 42*
Treatment and variable Adjusted HR [95% CI] P
AL (n = 14,679; 371 recrudescences)
Age category: 12 years (reference)
< 1 1.55 (0.86–2.78) 0.150
1 to < 5 2.38 (1.51–3.75) < 0.001
5 to < 12 1.39 (0.86–2.23) 0.160
Enrollment parasite density (log scale) 1.13 (1.05–1.23) 0.002
Lumefantrine dose (mg/kg) 1.00 (0.99–1.01) 0.860
ASAQ (n = 7,652; 220 recrudescences)
Age category: 12 years (reference)
< 1 2.20 (1.01–4.78) 0.047
1 to < 5 2.27 (1.13–4.55) 0.021
5 to < 12 1.51 (0.72–3.17) 0.140
Enrollment parasite density (log scale) 1.50 (1.16–1.93) 0.002
Amodiaquine dose (mg/kg) 0.92 (0.82–1.04) 0.180
Drug formulation: fixed dose (reference)
Co-blistered 0.98 (0.41–2.32) 0.960
Loose 2.94 (1.58–5.48) 0.001
* Risk factors were selected based upon previous analysis of the same data set (“The effect of dosing strategies on the
antimalarial efficacy of artemether-lumefantrine: a pooled analysis of individual patient data, by the WWARN AL Study
Group” presubmission approved at PLoS Medicine, March 28, 2014 and “The Effect of Dosing Strategies on the Therapeutic
Efficacy of Artesunate Amodiaquine for uncomplicated malaria: A Pooled Analysis of Individual Patient Data” presubmission
planned for PLoS Medicine before April 15, 2014). Values in bold are statistically significant. PCR = polymerase chain
reaction; HR = hazards ratio; CI = confidence interval; AL = artemether-lumefantrine; ASAQ = artesunate-amodiaquine.
TABLE 2
PCR-adjusted adequate clinical and parasitologic response for patients treated with of artemether-lumefantrine and artesunate-
amodiaquine after 42 days of follow-up*
Variable AL ASAQ fixed dose and co-blistered ASAQ loose No. at risk 4,763 1,113 986 ACPR by group, % (95% CI)
Age category, years
< 1 96.7 (92.7–98.5) 100 85.2 (70.5–93.0)
1 to < 5 93.6 (92.0–94.8) 96.4 (93.2–98.1) 93.8 (90.0–96.2)
5–12 96.3 (94.5–97.5) 98.8 (91.6–99.8) 99 (96.1–99.8)
12 95.2 (93.8–96.3) – –
Region
Asia/Oceania 95.2 (93.8–96.2) – – East Africa 93.8 (92.4–95.0) 100† 91.2 (88.0–94.7) West Africa 96.2 (94.6–97.3) 96.9 (94.2–98.3) 99.2 (96.8–99.8)†
Overall 94.8 (94.0–95.5) 97.0 (94.4–98.4) 93.0 (89.2–95.6)
* PCR = polymerase chain reaction; ACPR = adequate clinical and parasitologic response; AL = artemether-lumefantrine;
ASAQ = artesunate –amodiaquine; CI = confidence interval.
† Followed-up to day 28.
TABLE 3
Baseline (pre-treatment) prevalence of genetic markers associated with drug resistance*
Marker Asia/Oceania East Africa West Africa pfcrt 76
Sample size 84 1,708 1,848 K 3 (4) 553 (32) 494 (27) K/T 2 (2) 125 (7) 249 (13) T 79 (94) 1,030 (60) 1105 (60) pfcrt 72–76
Sample size 84 155 84 CVMNK 3 (4) 37 (24) 14 (17) CVIET 0 117 (75) 53 (63) SVMNT 79 (94) 0 0 Mixed 2 (2) 1 (1) 17 (20) pfmdr1 86
Sample size 226 2,033 1,321 N 160 (71) 759 (37) 678 (51) N/Y 0 378 (19) 190 (14) Y 66 (29) 896 (44) 453 (34) pfmdr1 184
Sample size 228 1,275 686 Y 183 (80) 803 (63) 287 (42) Y/F 8 (4) 130 (10) 77 (11) F 37 (16) 342 (27) 322 (47) Sample size 77 1,017 687 D 67 (87) 454 (45) 526 (77) D/Y 10 (13) 309 (30) 86 (13) Y 0 254 (25) 75 (11) pfmdr1 86 + 1246
Sample size 69 1,000 685 N D 12 (17) 129 (13) 263 (38) N Y 0 9 (1) 2 (0) Y D 50 (72) 248 (25) 199 (29) Y Y 0 220 (22) 71 (10) Mixed 7 (10) 394 (39) 150 (22) pfmdr1 copy number
Sample size 176 659 0 1 88 (50) 642 (98) 0 2 57 (32) 16 (2) 0 > 2 31 (18) 1 (0) 0
* Values are no. (%). pfcrt = Plasmodium falciparum chloroquine resistance transporter gene; pfmdr1 = P. falciparum
multidrug resistance 1 (pfmdr1) gene.
TABLE 4
Multivariable risk factors for PCR-adjusted recrudescent infections of persons treated with artemether-lumefantrine on day 42*
Marker and variable Adjusted hazard ratio (95% CI) P pfmdr1 86 (n = 2,543; 135 recrudescent infections)†
pfmdr1 N86 or N/Y
In recrudescence up to day 14 0.79 (0.25–2.54) 0.694 In recrudescence between days 14 and 28 4.74 (2.29–9.78) < 0.001 In recrudescence after day 28 0.84 (0.43–1.66) 0.624 Enrollment parasite density (loge – scale) 1.13 (0.99–1.29) 0.056 Age category (reference < 1 year)
1 to < 5 1.05 (0.40–2.75) 0.922 5 to < 12 0.85 (0.30–2.38) 0.752 12 0.77 (0.25–2.36) 0.647 Lumefantrine dose (mg/kg) 0.99 (0.98–1.00) 0.109 pfmdr1 copy number (n = 808; 73 recrudescent infections)
pfmdr1 copy number > 1‡
In recrudescence up to day 14 83.56 (7.43–939.70) < 0.001 In recrudescence between days 14 and 21 18.54 (7.61–45.19) < 0.001 In recrudescence after day 21 0.61 (0.25–1.51) 0.286 Region (reference Africa)
Asia/Oceania 5.09 (1.06–24.38) 0.042 Enrollment parasite density (loge – scale) 1.00 (0.85–1.18) 0.978 Age category (reference < 5 years)
5 to < 12 0.62 (0.22–1.77) 0.368 12 0.56 (0.16–1.93) 0.359 Lumefantrine dose (mg/kg) 0.98 (0.96–1.00) 0.113 pfmdr1 86 and copy number (n = 719; 59 recrudescent infections)§
pfmdr1 N86 or N/Y
In recrudescence up to day 14 1.00 (0.07–13.64) 0.997 In recrudescence between days 14 and 28 5.98 (1.68–21.36) 0.006 In recrudescence after day 28 0.51 (0.18–1.47) 0.21 pfmdr1 copy number > 1
In recrudescence up to day 14 2.17 (0.16–29.77) 0.561 In recrudescence between days 14 and 21 6.52 (2.36–17.97) < 0.001 In recrudescence after day 21 0.94 (0.31–2.82) 0.916 Enrollment parasite density (loge – scale) 1.08 (0.92–1.28) 0.348 Age category (reference < 5 years)
5 to < 12 1.46 (0.59–3.57) 0.413 12 0.79 (0.27–2.33) 0.663 Lumefantrine dose (mg/kg) 0.98 (0.95–1.00) 0.05
* Values in bold are statistically significant. PCR = polymerase chain reaction; CI = confidence interval; pfmdr1 = P.
falciparum multidrug resistance 1 (pfmdr1) gene.
† Region not included as a covariate or interaction term with pfmdr1 86 genotype because proportional hazards
assumption was not met.
‡ Sparse data for pfmdr1 copy number in Africa prevented the inclusion of region as an interaction term.
§ Region as a covariate and region-genotype interaction terms did not have statistically significant effects in this
model.
TABLE 5
Selection of pfcrt and pfmdr1 genotypes after treatment with artemether-lumefantrine and artesunate-amodiaquine*
Marker Genotype Recurrence Recrudescence Re-infection AL ASAQ AL ASAQ AL ASAQ
pfcrt 76 K T† 16% (89/571) 10% (25/237) 5% (4/73) 20% (7/35) 17% (82/493) 9% (17/196)
T K 30% (171/571) 8% (18/237) 25% (18/73) 11% (4/35) 31% (152/493) 7% (14/196) No change 54% (311/571) 82% (194/237) 70% (51/73) 69% (24/35) 53% (259/493) 84% (165/196)
< 0.001 0.286 0.004 (exact) 0.366 < 0.001 0.590
pfmdr1 86 N Y 13% (95/712) 27% (92/341) 10% (10/101) 18% (5/28) 14% (85/609) 28% (87/308)
Y N 40% (286/712) 16% (54/341) 31% (31/101) 14% (4/28) 42% (255/609) 16% (49/308) No change 46% (331/712) 57% (195/341) 59% (60/101) 68% (19/28) 44% (269/609) 56% (172/308)
< 0.001 0.002 0.001 0.739 < 0.001 0.001 pfmdr1 184 Y F 24% (74/311) 12% (37/303) 20% (14/69) 12% (3/25) 25% (60/242) 12% (34/273)
F Y 16% (51/ 311) 17% (50/303) 14% (10/69) 4% (1/25) 17% (41/242) 18% (49/273) No change 60% (186/311) 71% (216/303) 65% (45/69) 84% (21/25) 58% (141/242) 70% (190/273)
0.040 0.163 0.414 0.625 0.059 0.100
pfmdr1 1246 D Y 14% (38/273) 32% (102/317) 11% (5/44) 39% (11/28) 15% (33/227) 32% (90/284)
Y D 32% (86/273) 19% (60/317) 30% (13/44) 14% (4/28) 32% (73/227) 20% (56/284) No change 54% (149/273) 49% (155/317) 59% (26/44) 46% (13/28) 53% (121/227) 48% (138/284)
< 0.001 0.001 0.059 0.119 < 0.001 0.005
pfmdr1 copy
number 1 2 or more 1% (2/269) – 4% (2/53) – 0 –
2 or more 1 1% (3/269) – 2% (1/53) – 1% (2/216) – No change 98% (264/269) – 94% (50/53) – 99% (214/216) –
1.000 (exact) 1.000 (exact) 0.500 (exact)
* Values in bold indicate statistically significant selection (P < 0.05) by using McNemar’s paired test. Those marked exact were
tested by using the exact distribution for small sample sizes. A small number of recurrent infections (4 for AL and 6 for ASAQ)
were not polymerase chain reaction–adjusted and were excluded from the analysis of recrudescent and re-infections. pfcrt =
Plasmodium falciparum chloroquine resistance transporter gene; pfmdr1 = P. falciparum multidrug resistance 1 (pfmdr1) gene; AL
= artemether-lumefantrine; ASAQ = artesunate –amodiaquine.
† Each category includes all changes from one allele to another. For example, K T includes K T, K K/T, and K/T T
changes.
SUPPLEMENTAL TABLE 1
Univariable and multivariable risk factors for PCR-adjusted recrudescence at day 42 (n = 14,679 and 371 recrudescences) for patients treated with artemether-lumefantrine*
Variable No. Univariate analysis Multivariate analysis Population attributable risk†
Crude HR (95% CI) P Adjusted HR (95% CI) P Frequency PAR
Age (years) 14,679 0.96 (0.94–0.98) < 0.001 – – – –
Weight (kg) 14,769 0.98 (0.97–0.99) < 0.001 – – – –
Lumefantrine dose (mg/kg) 14,769 1 (0.99–1) 0.550 1 (0.99–1.01) 0.860 28.13% 2.26%
Clinical variables
Baseline parasitemia (log scale) 14,769 1.15 (1.07–1.25) < 0.001 1.13 (1.05–1.23) 0.002 9.12% 4.15%‡
Baseline parasitemia > 100,000/L 14,769 1.55 (1.15–2.09) 0.004 – – – –
Baseline gametocytemia 7,659 1.55 (1.04–2.32) 0.031 – – – –
Age category 14,679
12 years (reference)
< 1 1.74 (1.01–3) 0.045 1.55 (0.86–2.78) 0.150 9.01% 5.68%
1 to < 5 2.69 (1.73–4.17) < 0.001 2.38 (1.51–3.75) < 0.001 45.72% 41.24%‡
5 to <12 1.51 (0.95–2.38) 0.079 1.39 (0.86–2.23) 0.160 20.63% 9.21%
Weight category 14,769
35 kg (reference)
5 to < 15 2.47 (1.58–3.88) < 0.001 – – – –
15 to < 25 1.92 (1.21–3.04) 0.005 – – – –
25 to < 35 1.39 (0.75–2.56) 0.300 – – – –
Supervision 14,396
Full (reference) – – – –
Partial 0.92 (0.51–1.67) 0.790 – – – –
Unsupervised 1.66 (0.56–4.93) 0.370 – – – –
Co-administration with fat 7,180 – – – –
With fatty meal (reference)
Without fatty meal 0.91 (0.32–2.61) 0.860 – – – –
* Values in bold are statistically significant. PCR = polymerase chain reaction; HR = hazards ratio; CI = confidence interval; PAR population attributable risk.
† Overall PAR for model: 52.9% calculated as calculated as .
‡ Cumulative PAR for hyper-parasitemia and age 1 to < 5 years: 43.7.
SUPPLEMENTAL TABLE 2
Univariable and multivariable risk factors for PCR-adjusted recrudescenceat day 42 (n = 7,652 and 220 recrudescences for final model) for patients treated with artesunate-
amodiaquine*
Variable No. (no.)† Univariable analysis Multivariable analysis PAR‡
Crude HR (95% CI) P Adjusted HR (95% CI) P Frequency PAR
Amodiaquine dose (mg/kg) (5 units) 7,652 (220) 0.90 (0.8–1.01) 0.081 0.92 (0.82–1.04) 0.180 – –
Clinical variables
Parasitemia (log scale) 8,224 (223) 1.53 (1.19–1.97) < 0.001 1.5 (1.16–1.93) 0.002 10.7% 5.5%
Pfmicl > 100,000/L 8,224 (223) 1.54 (1.03–2.30) 0.034 – – – –
Baseline fever (temperature > 37.5C) 7,847 (212) 0.87 (0.64–1.20) 0.400 – – – –
Baseline hemoglobin level 5,708 (193) 0.93 (0.86–1.00) 0.054 – – – –
Baseline anemia (hemoglobin level < 10) 5,708 (193) 1.37 (1.00–1.89) 0.050 – – – –
Baseline gametocyte level 4,258 (91) 1.41 (0.76–2.59) 0.270 – – – –
Species at enrollment
Pure P. falciparum infection (reference) 8,189 (220)
Mixed infections 35 (3) 1.28 (0.37–4.41) 0.700 – – – –
Sex
F (reference) 3,755(106)
M 4,308 (102) 0.87 (0.66–1.15) 0.340 – – – –
Age category
12 yrs (reference) 1,289 (14)
< 1 693 (32) 3.52 (1.65–7.5) 0.001 2.2 [1.01–4.78] 0.047 8.4% 11.1%
1 to < 5 4,816 (158) 3.48 (1.78–6.82) < 0.001 2.27 [1.13–4.55] 0.021 58.5% 46.9%
5 to < 12 1,426 (19) 1.72 (0.84–3.54) 0.140 1.51 [0.72–3.17] 0.280 – –
Drug formulation
FDC (reference) 4,212 (78)
nFDC co-blistered 900 (11) 1.19 (0.51–2.78) 0.700 0.98 [0.41–2.32] 0.960 – –
nFDC loose 3,112 (134) 3.00 (1.64–5.50) < 0.001 2.94 [1.58–5.48] 0.001 36.3% 41.9%
Treatment supervision 8,334
Fully (reference) 6,287 (74)
Partial 1,937(149) 2.08 (0.79–5.46) 0.140 – – – –
* Values in bold are statistically significant. PCR = polymerase chain reaction; HR = hazards ratio; CI = confidence interval; FDC = fixed-dose combination.
† No. = number of patients (No.); no. = number of PCR-confirmed treatment failures.
‡ Overall PAR for the model accounted by significant variables: 74.0% calculated as . For PAR calculation, parasitemia was categorized at 100,000/L.
Variance of the random effect = 0.914. Anemia was not kept for multivariable analysis because of missing values. The coefficients for other covariates remain unaffected with or
without anemia in the model. The assumption of proportional hazard held true for overall final multivariable model globally (P = 0.584) and individually for each of the covariates (P >
0.05).
SUPPLEMENTAL TABLE 3
Summary of studies included in the analysis*
Region, country Reference Study year(s) Treatment (no.) Transmission zone (no.)
AL ASAQ High Moderate Low
East Africa
Ethiopia 67 2006 34 34
Ethiopia 68 2008–2009 348 348
Kenya Unpublished 2007-2008 54 54
Kenya 69 2005 241 241
Kenya 15 2007 103 103
Madagascar 70 2006–2007 17 1 15 1
Sudan 31 2006 91 91
Sudan 16 2003 80 80
Tanzania 71 2007–2008 359 359
Tanzania 11 2007 244 244
Tanzania 72 2010 108 108
Tanzania (Zanzibar) 47 2002–2003 208 208
Tanzania (Zanzibar) 27 2002–2003 200 200
Tanzania (Zanzibar) 25 2002–2003 † †
Tanzania 73 2004 50 50
Uganda 74 2004–2007 149 149 298
Uganda 26 2005 204 204
Uganda 46 2005 204 204
Uganda Unpublished 2007–2008 112 112
West Africa
Benin Unpublished 2007 96 95 191
Burkina Faso 30 2006 188 188
Burkina Faso Unpublished 2004–2006 890 890
Burkina Faso 75 2005 261 261
Guinea-Bissau 76 2006–2008 191 191
Liberia 77 2009 150 149 299
Mali 2009 337 188 77 72
Mali 49 2002–2004 252 252
Nigeria 78 2007–2008 47 45 92
Oceania
Papua New Guinea 79 2005–2007 176 176
Asia
Thailand 34 1995–2002 1,417 1,417
Thailand 29 1995–2002 † †
Total 5,003 2,246 3,136 2,016 2,097
* AL = artemether-lumefantrine; ASAQ = artesunate-amodiaquine.
† Samples overlap with previous study.
SUPPLEMENTAL TABLE 4
Baseline characteristics of patients treated with artemether-lumefantrine or artesunate-amodiaquine*
Treatment, variable Asia/Oceania East Africa West Africa Overall
AL
No. (%) 1,593 (31.9) 2,140 (42.8) 1,270 (25.3) 5,003
Study period 1995–2007 2002–2010 2003–2009 1995–2010
Follow-up (days)
28 19.5% 32.6% 50.8% 33.0%
42 56.2% 39.3% 49.2% 47.2%
43–63 24.4% 28.0% 19.7%
Median age (IQR, range) (years) 18 (10–30, 0.8–70) 3 (2–5, 0.3–81) 4 (3–7, 0.3–61) 5 (3–14, 0.3–81)
< 1 0.1% 7.7% 3.4% 4.2%
1 to < 5 12.1% 59.9% 47.7% 41.6%
5–11 17.4% 18.6% 38.3% 23.2%
12 70.5% 13.6% 9.7% 30.7%
Missing 0.0% 0.3% 0.9% 0.4%
Baseline parasites/µL geometric mean (95% CI) 5,371 (4,833–5,969) 14,094 (13,015–15,262) 25,066 (23,411–26,838) 12,086 (11,459–12,748)
Supervision
Full 64.6% 35.8% 62.2% 51.7%
Partial 11.0% 44.4% 34.1% 31.2%
Unsupervised 0.0% 19.8% 0.0% 8.5%
Not stated/unknown 24.4% 0.0% 3.7% 8.7%
Co-administration
With food 11.0% 20.7% 26.9% 19.2%
Advised to consume fatty food 0.0% 46.0% 0.0% 19.7%
None 0.0% 9.5% 0.0% 4.1%
Not stated 89.0% 23.7% 73.1% 57.0%
ASAQ
No. (%) 815 (36.3%) 1,431 (63.7%) 2,246
Study period 2002–2008 2002–2009 2002–2009
Follow-up, days
28 74.5% 82.9% 79.9%
42 25.5% 17.1% 20.1%
Median age (IQR, range) (years) 3 (2–5, 0.4–60) 3 (2–4, 0.4–38) 3 (2–4, 0.4–60)
< 1 8.3% 7.7% 7.9%
1 to < 5 65.3% 78.6% 73.8%
5–11 19.1% 12.6% 15.0%
12 7.0% 1.0% 3.2%
Missing 0.2% 0.0% 0.1%
Baseline parasites/µL geometric mean (95% CI) 18,412 (16,480–20,569) 15,661 (14,593–16,806) 16,608 (15,635–17,642)
Formulation
Fixed dose 3.2% 48.1% 31.8%
Non-fixed dose 94.7% 51.9% 67.4%
Supervision
Full 63.8% 62.2% 62.8%
Partial 17.1% 10.9%
Not stated/unknown 36.2% 20.8% 26.4%
* AL = artemether-lumefantrine; IQR = interquartile range; CI, confidence interval; ASAQ = artesunate-amodiaquine.
Figure 1
0.85
0.90
0.95
1.00
Kap
lan-
Mei
er s
urvi
val e
stim
ates
0 10 20 30 40Days
pfmdr1 86Ypfmdr1 N86 or N/Y
AL efficacy by pfmdr1 86
0.80
0.70
0.90
1.00
Kap
lan-
Mei
er s
urvi
val e
stim
ates
0 10 20 30 40
Days
Single copyMultiple copies
AL efficacy by pfmdr1 copy number
A
B
Figure 2
00.
020.
040.
060.
080.
10
Cum
ulat
ive
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denc
e of
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ion
0 10 20 30 40Days since enrollment
pfcrt K76 or K/Tpfcrt 76T
00.
20.
40.
60.
81.
0
Rel
ativ
e in
cide
nce
of re
-infe
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n0 10 20 30 40
Days since enrollment
pfcrt K76 or K/Tpfcrt 76T
00.
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100.
15
Cum
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ive
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e of
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ion
0 10 20 30 40Days since enrollment
pfmdr1 N86 or N/Ypfmdr1 86Y
00.
20.
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60.
81.
0
Rel
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e in
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nce
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pfmdr1 N86 or N/Ypfmdr1 86Y
00.
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06
Cum
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ive
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denc
e of
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ion
0 10 20 30 40Days since enrollment
pfmdr1 D1246 or D/Ypfmdr1 1246Y
00.
20.
40.
60.
81.
0
Rel
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e in
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nce
of re
-infe
ctio
n
0 10 20 30 40Days since enrollment
pfmdr1 D1246 or D/Ypfmdr1 1246Y
Figure 3a
00.
050.
100.
150.
200.
25
Cum
ulat
ive
inci
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e of
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ion
0 10 20 30 40Days since enrollment
pfcrt 76T or K/Tpfcrt K76
00.
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81.
0
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-infe
ctio
n0 10 20 30 40
Days since enrollment
pfcrt 76T or K/Tpfcrt K76
00.
050.
100.
150.
200.
25
Cum
ulat
ive
risk
of re
-infe
ctio
n
0 10 20 30 40Days since enrollment
pfmdr1 86Y or N/Ypfmdr1 N86
00.
20.
40.
60.
81.
0
Rel
ativ
e ris
k of
re-in
fect
ion
0 10 20 30 40Days since enrollment
pfmdr1 86Y or N/Ypfmdr1 N86
00.
050.
100.
15
Cum
ulat
ive
risk
of re
-infe
ctio
n
0 10 20 30 40Days since enrollment
pfmdr1 1246Y or D/Ypfmdr1 D1246
00.
20.
40.
60.
81.
0
Rel
ativ
e ris
k of
re-in
fect
ion
0 10 20 30 40Days since enrollment
pfmdr1 1246Y or D/Ypfmdr1 D1246
Figure 3b
![[INTERNET] EnquXte publique lac d'Hossegor … · ... EnquXte publique lac d'Hossegor bernard.rebouillat@wanadoo.fr](https://img.pdfslide.us/doc/110x75/5ac8c1d97f8b9acb688ccf6d/internet-enquxte-publique-lac-dhossegor-enquxte-publique-lac-dhossegor.jpg)
